Research a Competitor in One Prompt
The most popular exercise from our live workshop. One prompt, one competitor analysis, the kind of output that used to take two weeks and five figures.
You can build a structured competitor analysis in under 5 minutes using a single AI prompt. The trick is asking for specific, verifiable business details rather than vague summaries. AI handles the structure and research synthesis. You handle the fact-checking.
This is the hands-on post. We're building something you can actually use. By the end, you'll have a competitor analysis template you can reuse any time you need to evaluate a new competitor, prepare for a pitch, or brief your team.
In our ZeroShot Studio workshops, this exercise consistently got the biggest reaction. People walked in skeptical. Five minutes later they were staring at a structured competitor breakdown that would have taken a junior analyst half a day to produce. (With one important caveat: you still need to verify the details. More on that below.)
What are we building?
A one-prompt competitor analysis that covers the key business dimensions you actually care about. Not a 50-page market report. Not a surface-level paragraph. Something in between: structured, actionable, and fast enough that you'll actually use it regularly.
The goal is a report you can paste into a slide, share in a team Slack channel, or use as a briefing document before a sales call. Something that answers: "What does this competitor do, how do they position themselves, and where are the gaps?"
What are the six competitor analysis dimensions?
Every competitor analysis worth reading covers six dimensions. These aren't random. They map to the questions founders and board members actually ask when evaluating competitive positioning:
- Core offering -- What do they sell? Who do they sell it to? What problem does it solve?
- Pricing model -- How do they charge? Subscription, one-time, freemium, usage-based? What are the price points?
- Target market -- Who's their ideal customer? What size business? What industry? What geography?
- Key differentiators -- What do they claim makes them different? What do their customers say makes them different (check reviews)?
- Weaknesses and gaps -- Where do customers complain? What features are missing? Where do they lose deals?
- Market position -- Are they the leader, a challenger, a niche player? How does their positioning compare to yours?
These six fields give you a complete picture without drowning in unnecessary detail. In our workshops, we tested both shorter and longer frameworks. Six fields hit the sweet spot: comprehensive enough to be useful, concise enough to actually read.
[IMAGE: Six business detail fields shown as a hexagonal framework]
- Type: diagram
- Filename: competitor-six-fields.png
- Alt text: A hexagonal diagram showing the six competitor analysis fields: core offering, pricing, target market, differentiators, weaknesses, and market position
- Caption: Six dimensions. One prompt. Five minutes.
What does the full prompt template look like?
Here's the template. Copy it, replace the bracketed sections with your details, and paste it into ChatGPT, Claude, or Gemini.
You are a business analyst specialising in competitive intelligence.
I run [YOUR BUSINESS DESCRIPTION: e.g. "a 15-person SaaS company selling project management tools to creative agencies in Australia"].
Analyse [COMPETITOR NAME] as a competitor to my business. Cover these six areas:
1. CORE OFFERING: What they sell, who they sell to, what problem they solve. Be specific about product features.
2. PRICING: Their pricing model and price points. Include free tier details if applicable.
3. TARGET MARKET: Their ideal customer profile. Company size, industry, geography, use case.
4. DIFFERENTIATORS: What makes them stand out. Include both their marketing claims and what their customers actually say (based on review sites, social media sentiment).
5. WEAKNESSES: Where they fall short. Common customer complaints, missing features, known limitations.
6. MARKET POSITION: Leader, challenger, or niche player. Estimated market share if available. How they compare to my business specifically.
Format: Use the six headers above. Each section should be 2-3 sentences with specific details, not generalities. Include any relevant numbers (revenue, customer count, pricing) where available.
Flag any claims you're not confident about with [UNVERIFIED].
Constraints: Stick to publicly available information. Don't invent data points. If you don't know something, say so rather than guessing.
Notice how this uses the RCTFC framework from Post 2: Role (business analyst), Context (your business), Task (analyse competitor), Format (six sections), Constraints (flag uncertainty).
The "[UNVERIFIED]" flag instruction is the most important line in this template. It forces the model to distinguish between things it's confident about and things it's guessing.
How do you read and use the output?
When you run this prompt, you'll get a structured report covering all six dimensions. Here's how to use it effectively:
First pass: scan for flags. Look for any "[UNVERIFIED]" markers. These are the claims you need to check before sharing with anyone. In testing, models typically flag 2-4 items per analysis, which is honest and useful.
Second pass: check the numbers. Any specific revenue figures, customer counts, or pricing details should be verified against the competitor's website, press releases, or review platforms like G2 and Capterra. AI sometimes pulls outdated pricing or estimates revenue from old articles.
Third pass: assess relevance. Not every dimension will be equally important for your situation. If you're competing on price, the pricing section matters most. If you're competing on features, focus on differentiators and weaknesses.
The output format is designed to be shareable. You can paste it into a Notion doc, a Google Doc, or a Slack message. It reads well because it's structured, not because it's polished prose.
When I ran this exercise with a group of startup founders, one participant analysed a competitor she'd been tracking informally for months. "It found a weakness I'd never noticed," she told me. The AI had flagged negative reviews about the competitor's onboarding process, something that didn't show up in the competitor's marketing but appeared repeatedly on G2 reviews.
Why is fact-checking non-negotiable?
This is the part where I get serious for a moment. AI competitor analysis is a starting point, not a finished product. Here's why:
- Revenue and customer numbers are frequently wrong. Models estimate based on old data, press coverage, or employee count proxies. Always verify.
- Pricing changes constantly. Check the competitor's actual pricing page. It takes 30 seconds.
- Market positioning is subjective. The model's assessment of "leader vs challenger" may not match your industry's actual dynamics.
- Training data has a cutoff. If the competitor launched a new product last month, the model might not know about it.
A 2025 study by Stanford's AI Index found that LLMs achieve roughly 60-80% accuracy on factual business claims, varying by how public and well-documented the company is (Stanford HAI AI Index Report). Larger, publicly traded companies get more accurate results. Small startups get more guesswork.
The rule: Use AI for structure and speed. Use your own eyes for verification. A 5-minute AI draft plus 10 minutes of fact-checking still beats 3 hours of manual research.
Never share an AI-generated competitor analysis without spending 10 minutes verifying the specific claims. Structure is free. Accuracy requires your eyeballs.
How do you iterate and go deeper?
Once you have the initial analysis, you can go deeper with follow-up prompts:
Go deeper on a specific dimension:
"Expand on the WEAKNESSES section. Search for common themes in negative G2 reviews and Reddit discussions about [COMPETITOR]. Give me 5 specific complaints with the approximate frequency of each."
Compare two competitors:
"Now do the same analysis for [COMPETITOR 2]. Then add a comparison table showing how both competitors compare to my business across all six dimensions."
Generate strategic recommendations:
"Based on the analysis of [COMPETITOR], suggest 3 specific actions my business could take to differentiate. Focus on gaps in their offering that align with our strengths."
Create a one-page brief:
"Condense the full analysis into a 200-word executive summary suitable for a board meeting. Lead with the biggest threat and biggest opportunity."
Pro tip: save your best competitor analysis prompts in a document. When a new competitor appears, you can run the same template in minutes. Several workshop participants told us they now run this monthly on their top 3-5 competitors.
[IMAGE: Screenshot showing a competitor analysis output with verification annotations]
- Type: screenshot
- Filename: competitor-output-annotated.png
- Alt text: A competitor analysis output with green checkmarks on verified facts and orange flags on claims that need checking
- Caption: The raw output is step one. Verification turns it into something you can trust.
FAQ
Claude and GPT-4o both perform well. Claude tends to be more conservative with unverified claims (it flags more uncertainty, which is actually useful). GPT-4o sometimes provides more specific numbers but with lower reliability. For competitor research, being cautious is better.
Some models with web browsing enabled (ChatGPT with browsing, Gemini) can pull current information. Models without browsing rely on training data, which may be 6-12 months old. If timeliness matters, use a model with browsing or do the final verification yourself.
The less public information exists about a company, the less accurate AI will be. For small or new competitors, expect more "[UNVERIFIED]" flags and do more manual verification. For very early-stage competitors, you might be better off checking their website, LinkedIn, and Product Hunt manually.
Completely. You're analysing publicly available information, the same thing any business analyst would do manually. You're not accessing private data, scraping behind paywalls, or doing anything the competitor hasn't already made public.
Yes, and this connects directly to Post 4: Agents & No-Code. You could set up a monthly trigger that runs competitor analyses and drops updated reports into a shared folder. Several workshop participants built exactly this.
Next up: Your Team Is Already Using AI. Here's How to Not Get Burned -- the security and GDPR post your legal team will thank you for reading.
This is Post 5 of 7 in the AI for Business free course. Previous: Agents & No-Code